Overview

Dataset statistics

Number of variables14
Number of observations715
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory155.9 KiB
Average record size in memory223.3 B

Variable types

Text1
Numeric10
Categorical3

Alerts

AveRPO is highly overall correlated with AveRPW and 2 other fieldsHigh correlation
AveRPW is highly overall correlated with AveRPO and 3 other fieldsHigh correlation
HS is highly overall correlated with AveRPO and 3 other fieldsHigh correlation
Inns is highly overall correlated with Lost and 3 other fieldsHigh correlation
Lost is highly overall correlated with Inns and 3 other fieldsHigh correlation
Mat is highly overall correlated with Inns and 3 other fieldsHigh correlation
Runs is highly overall correlated with HS and 4 other fieldsHigh correlation
W/L is highly overall correlated with AveRPO and 3 other fieldsHigh correlation
Won is highly overall correlated with AveRPW and 5 other fieldsHigh correlation
Tied is highly imbalanced (78.3%)Imbalance
NR is highly imbalanced (65.9%)Imbalance
Won has 99 (13.8%) zerosZeros
Lost has 60 (8.4%) zerosZeros
W/L has 99 (13.8%) zerosZeros
LS has 224 (31.3%) zerosZeros

Reproduction

Analysis started2024-10-28 06:46:54.180565
Analysis finished2024-10-28 06:47:21.532717
Duration27.35 seconds
Software versionydata-profiling v4.8.3
Download configurationconfig.json

Variables

Team
Text

Distinct105
Distinct (%)14.7%
Missing0
Missing (%)0.0%
Memory size45.3 KiB
2024-10-28T12:17:22.147410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length10
Mean length7.7076923
Min length4

Characters and Unicode

Total characters5511
Distinct characters54
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6 ?
Unique (%)0.8%

Sample

1st rowZimbabwe
2nd rowZimbabwe
3rd rowZimbabwe
4th rowZimbabwe
5th rowZimbabwe
ValueCountFrequency (%)
pakistan 20
 
2.4%
england 19
 
2.3%
south 19
 
2.3%
india 19
 
2.3%
sri 19
 
2.3%
indies 19
 
2.3%
lanka 19
 
2.3%
west 19
 
2.3%
australia 17
 
2.0%
bangladesh 17
 
2.0%
Other values (110) 648
77.6%
2024-10-28T12:17:23.305013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 889
16.1%
n 477
 
8.7%
i 387
 
7.0%
e 371
 
6.7%
r 254
 
4.6%
l 245
 
4.4%
t 212
 
3.8%
d 209
 
3.8%
s 206
 
3.7%
o 165
 
3.0%
Other values (44) 2096
38.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5511
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 889
16.1%
n 477
 
8.7%
i 387
 
7.0%
e 371
 
6.7%
r 254
 
4.6%
l 245
 
4.4%
t 212
 
3.8%
d 209
 
3.8%
s 206
 
3.7%
o 165
 
3.0%
Other values (44) 2096
38.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5511
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 889
16.1%
n 477
 
8.7%
i 387
 
7.0%
e 371
 
6.7%
r 254
 
4.6%
l 245
 
4.4%
t 212
 
3.8%
d 209
 
3.8%
s 206
 
3.7%
o 165
 
3.0%
Other values (44) 2096
38.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5511
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 889
16.1%
n 477
 
8.7%
i 387
 
7.0%
e 371
 
6.7%
r 254
 
4.6%
l 245
 
4.4%
t 212
 
3.8%
d 209
 
3.8%
s 206
 
3.7%
o 165
 
3.0%
Other values (44) 2096
38.0%

Mat
Real number (ℝ)

HIGH CORRELATION 

Distinct24
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.0741259
Minimum1
Maximum24
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.7 KiB
2024-10-28T12:17:23.637769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q14
median6
Q39
95-th percentile16
Maximum24
Range23
Interquartile range (IQR)5

Descriptive statistics

Standard deviation4.2943307
Coefficient of variation (CV)0.60704754
Kurtosis1.1103929
Mean7.0741259
Median Absolute Deviation (MAD)2
Skewness1.1496313
Sum5058
Variance18.441276
MonotonicityNot monotonic
2024-10-28T12:17:23.938860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
4 101
14.1%
5 95
13.3%
3 83
11.6%
6 78
10.9%
7 48
 
6.7%
8 47
 
6.6%
10 39
 
5.5%
9 37
 
5.2%
2 32
 
4.5%
11 23
 
3.2%
Other values (14) 132
18.5%
ValueCountFrequency (%)
1 19
 
2.7%
2 32
 
4.5%
3 83
11.6%
4 101
14.1%
5 95
13.3%
6 78
10.9%
7 48
6.7%
8 47
6.6%
9 37
 
5.2%
10 39
 
5.5%
ValueCountFrequency (%)
24 2
 
0.3%
23 1
 
0.1%
22 1
 
0.1%
21 4
 
0.6%
20 1
 
0.1%
19 2
 
0.3%
18 9
1.3%
17 8
1.1%
16 11
1.5%
15 14
2.0%

Won
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct18
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.4111888
Minimum0
Maximum20
Zeros99
Zeros (%)13.8%
Negative0
Negative (%)0.0%
Memory size5.7 KiB
2024-10-28T12:17:24.247720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q35
95-th percentile9
Maximum20
Range20
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.9997924
Coefficient of variation (CV)0.87939792
Kurtosis2.4363237
Mean3.4111888
Median Absolute Deviation (MAD)2
Skewness1.348823
Sum2439
Variance8.9987542
MonotonicityNot monotonic
2024-10-28T12:17:24.528091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
2 120
16.8%
1 116
16.2%
3 105
14.7%
0 99
13.8%
4 70
9.8%
5 57
8.0%
6 49
6.9%
7 30
 
4.2%
9 22
 
3.1%
8 14
 
2.0%
Other values (8) 33
 
4.6%
ValueCountFrequency (%)
0 99
13.8%
1 116
16.2%
2 120
16.8%
3 105
14.7%
4 70
9.8%
5 57
8.0%
6 49
6.9%
7 30
 
4.2%
8 14
 
2.0%
9 22
 
3.1%
ValueCountFrequency (%)
20 1
 
0.1%
17 1
 
0.1%
15 1
 
0.1%
14 3
 
0.4%
13 1
 
0.1%
12 7
 
1.0%
11 8
 
1.1%
10 11
1.5%
9 22
3.1%
8 14
2.0%

Lost
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct17
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.4111888
Minimum0
Maximum18
Zeros60
Zeros (%)8.4%
Negative0
Negative (%)0.0%
Memory size5.7 KiB
2024-10-28T12:17:24.803838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q35
95-th percentile8
Maximum18
Range18
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.5456918
Coefficient of variation (CV)0.74627702
Kurtosis3.2452169
Mean3.4111888
Median Absolute Deviation (MAD)2
Skewness1.3436156
Sum2439
Variance6.4805469
MonotonicityNot monotonic
2024-10-28T12:17:25.160963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
2 135
18.9%
3 133
18.6%
1 102
14.3%
4 88
12.3%
5 75
10.5%
0 60
8.4%
6 41
 
5.7%
7 34
 
4.8%
8 18
 
2.5%
9 12
 
1.7%
Other values (7) 17
 
2.4%
ValueCountFrequency (%)
0 60
8.4%
1 102
14.3%
2 135
18.9%
3 133
18.6%
4 88
12.3%
5 75
10.5%
6 41
 
5.7%
7 34
 
4.8%
8 18
 
2.5%
9 12
 
1.7%
ValueCountFrequency (%)
18 1
 
0.1%
17 1
 
0.1%
14 1
 
0.1%
13 1
 
0.1%
12 4
 
0.6%
11 5
 
0.7%
10 4
 
0.6%
9 12
 
1.7%
8 18
2.5%
7 34
4.8%

Tied
Categorical

IMBALANCE 

Distinct4
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size40.6 KiB
0
657 
1
 
55
2
 
2
3
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters715
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 657
91.9%
1 55
 
7.7%
2 2
 
0.3%
3 1
 
0.1%

Length

2024-10-28T12:17:25.510682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-28T12:17:25.828283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 657
91.9%
1 55
 
7.7%
2 2
 
0.3%
3 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 657
91.9%
1 55
 
7.7%
2 2
 
0.3%
3 1
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 715
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 657
91.9%
1 55
 
7.7%
2 2
 
0.3%
3 1
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 715
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 657
91.9%
1 55
 
7.7%
2 2
 
0.3%
3 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 715
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 657
91.9%
1 55
 
7.7%
2 2
 
0.3%
3 1
 
0.1%

NR
Categorical

IMBALANCE 

Distinct4
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size40.6 KiB
0
614 
1
88 
2
 
9
3
 
4

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters715
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 614
85.9%
1 88
 
12.3%
2 9
 
1.3%
3 4
 
0.6%

Length

2024-10-28T12:17:26.159549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-28T12:17:26.413428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 614
85.9%
1 88
 
12.3%
2 9
 
1.3%
3 4
 
0.6%

Most occurring characters

ValueCountFrequency (%)
0 614
85.9%
1 88
 
12.3%
2 9
 
1.3%
3 4
 
0.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 715
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 614
85.9%
1 88
 
12.3%
2 9
 
1.3%
3 4
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 715
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 614
85.9%
1 88
 
12.3%
2 9
 
1.3%
3 4
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 715
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 614
85.9%
1 88
 
12.3%
2 9
 
1.3%
3 4
 
0.6%

W/L
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct71
Distinct (%)9.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5403487
Minimum0
Maximum14
Zeros99
Zeros (%)13.8%
Negative0
Negative (%)0.0%
Memory size5.7 KiB
2024-10-28T12:17:26.678003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.34313725
median1
Q32
95-th percentile5
Maximum14
Range14
Interquartile range (IQR)1.6568627

Descriptive statistics

Standard deviation1.9359368
Coefficient of variation (CV)1.2568172
Kurtosis10.739538
Mean1.5403487
Median Absolute Deviation (MAD)0.66666667
Skewness2.8017482
Sum1101.3493
Variance3.7478512
MonotonicityNot monotonic
2024-10-28T12:17:27.096612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 99
13.8%
1 87
 
12.2%
2 54
 
7.6%
0.5 52
 
7.3%
3 39
 
5.5%
0.6666666667 36
 
5.0%
0.3333333333 33
 
4.6%
1.5 30
 
4.2%
4 27
 
3.8%
5 18
 
2.5%
Other values (61) 240
33.6%
ValueCountFrequency (%)
0 99
13.8%
0.1111111111 1
 
0.1%
0.125 2
 
0.3%
0.1428571429 2
 
0.3%
0.1666666667 6
 
0.8%
0.1818181818 1
 
0.1%
0.2 15
 
2.1%
0.25 15
 
2.1%
0.2857142857 4
 
0.6%
0.3 1
 
0.1%
ValueCountFrequency (%)
14 3
 
0.4%
11 2
 
0.3%
10 3
 
0.4%
9 6
 
0.8%
7 5
 
0.7%
6 12
1.7%
5.5 2
 
0.3%
5 18
2.5%
4.25 1
 
0.1%
4 27
3.8%

Runs
Real number (ℝ)

HIGH CORRELATION 

Distinct560
Distinct (%)78.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean948.46294
Minimum0
Maximum3902
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size5.7 KiB
2024-10-28T12:17:27.685083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile222
Q1474.5
median739
Q31298.5
95-th percentile2257.3
Maximum3902
Range3902
Interquartile range (IQR)824

Descriptive statistics

Standard deviation635.41473
Coefficient of variation (CV)0.66994155
Kurtosis0.98060821
Mean948.46294
Median Absolute Deviation (MAD)343
Skewness1.1466216
Sum678151
Variance403751.88
MonotonicityNot monotonic
2024-10-28T12:17:28.171512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
631 5
 
0.7%
756 4
 
0.6%
392 4
 
0.6%
508 3
 
0.4%
362 3
 
0.4%
1468 3
 
0.4%
279 3
 
0.4%
536 3
 
0.4%
517 3
 
0.4%
421 3
 
0.4%
Other values (550) 681
95.2%
ValueCountFrequency (%)
0 1
0.1%
72 1
0.1%
73 1
0.1%
98 1
0.1%
99 1
0.1%
100 1
0.1%
101 2
0.3%
103 1
0.1%
108 1
0.1%
124 1
0.1%
ValueCountFrequency (%)
3902 1
0.1%
3045 1
0.1%
3035 1
0.1%
2967 1
0.1%
2871 1
0.1%
2830 1
0.1%
2806 1
0.1%
2788 1
0.1%
2784 1
0.1%
2758 1
0.1%

Inns
Real number (ℝ)

HIGH CORRELATION 

Distinct25
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.9958042
Minimum0
Maximum24
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size5.7 KiB
2024-10-28T12:17:28.530325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q14
median6
Q39
95-th percentile16
Maximum24
Range24
Interquartile range (IQR)5

Descriptive statistics

Standard deviation4.261578
Coefficient of variation (CV)0.60916198
Kurtosis1.0897162
Mean6.9958042
Median Absolute Deviation (MAD)2
Skewness1.1505295
Sum5002
Variance18.161047
MonotonicityNot monotonic
2024-10-28T12:17:28.833244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
4 99
13.8%
5 98
13.7%
3 84
11.7%
6 84
11.7%
8 44
 
6.2%
7 42
 
5.9%
9 39
 
5.5%
10 39
 
5.5%
2 35
 
4.9%
13 24
 
3.4%
Other values (15) 127
17.8%
ValueCountFrequency (%)
0 1
 
0.1%
1 18
 
2.5%
2 35
 
4.9%
3 84
11.7%
4 99
13.8%
5 98
13.7%
6 84
11.7%
7 42
5.9%
8 44
6.2%
9 39
 
5.5%
ValueCountFrequency (%)
24 1
 
0.1%
23 2
 
0.3%
22 1
 
0.1%
21 2
 
0.3%
20 3
 
0.4%
19 1
 
0.1%
18 10
1.4%
17 7
1.0%
16 10
1.4%
15 16
2.2%

HS
Real number (ℝ)

HIGH CORRELATION 

Distinct175
Distinct (%)24.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean175.27972
Minimum0
Maximum344
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size5.7 KiB
2024-10-28T12:17:29.133474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile105.7
Q1150
median177
Q3200
95-th percentile239
Maximum344
Range344
Interquartile range (IQR)50

Descriptive statistics

Standard deviation39.968196
Coefficient of variation (CV)0.22802521
Kurtosis1.0538091
Mean175.27972
Median Absolute Deviation (MAD)24
Skewness-0.19206761
Sum125325
Variance1597.4567
MonotonicityNot monotonic
2024-10-28T12:17:29.587317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
176 14
 
2.0%
180 14
 
2.0%
185 13
 
1.8%
175 12
 
1.7%
158 11
 
1.5%
174 11
 
1.5%
132 10
 
1.4%
206 10
 
1.4%
199 10
 
1.4%
181 10
 
1.4%
Other values (165) 600
83.9%
ValueCountFrequency (%)
0 1
0.1%
53 1
0.1%
57 1
0.1%
58 1
0.1%
60 1
0.1%
72 2
0.3%
73 1
0.1%
74 1
0.1%
75 1
0.1%
77 2
0.3%
ValueCountFrequency (%)
344 1
 
0.1%
314 1
 
0.1%
297 1
 
0.1%
278 2
0.3%
268 1
 
0.1%
267 1
 
0.1%
263 1
 
0.1%
260 1
 
0.1%
259 1
 
0.1%
258 3
0.4%

LS
Real number (ℝ)

ZEROS 

Distinct145
Distinct (%)20.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean67.629371
Minimum0
Maximum200
Zeros224
Zeros (%)31.3%
Negative0
Negative (%)0.0%
Memory size5.7 KiB
2024-10-28T12:17:30.085953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median79
Q3110
95-th percentile147
Maximum200
Range200
Interquartile range (IQR)110

Descriptive statistics

Standard deviation53.510527
Coefficient of variation (CV)0.79123206
Kurtosis-1.2530017
Mean67.629371
Median Absolute Deviation (MAD)43
Skewness-0.029634821
Sum48355
Variance2863.3764
MonotonicityNot monotonic
2024-10-28T12:17:30.606188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 224
31.3%
95 11
 
1.5%
101 10
 
1.4%
111 9
 
1.3%
97 9
 
1.3%
127 9
 
1.3%
94 8
 
1.1%
102 7
 
1.0%
114 7
 
1.0%
76 7
 
1.0%
Other values (135) 414
57.9%
ValueCountFrequency (%)
0 224
31.3%
10 2
 
0.3%
18 1
 
0.1%
21 1
 
0.1%
23 1
 
0.1%
24 1
 
0.1%
26 1
 
0.1%
29 1
 
0.1%
30 4
 
0.6%
33 1
 
0.1%
ValueCountFrequency (%)
200 1
0.1%
193 1
0.1%
192 1
0.1%
187 1
0.1%
185 1
0.1%
178 1
0.1%
174 1
0.1%
173 2
0.3%
172 1
0.1%
171 1
0.1%

Season
Categorical

Distinct21
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size43.7 KiB
2024
83 
2022
72 
2023
65 
2023/24
61 
2021/22
57 
Other values (16)
377 

Length

Max length7
Median length4
Mean length5.434965
Min length4

Characters and Unicode

Total characters3886
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2018/19
2nd row2022/23
3rd row2017/18
4th row2015/16
5th row2022

Common Values

ValueCountFrequency (%)
2024 83
11.6%
2022 72
10.1%
2023 65
9.1%
2023/24 61
8.5%
2021/22 57
 
8.0%
2022/23 57
 
8.0%
2019/20 53
 
7.4%
2019 52
 
7.3%
2021 40
 
5.6%
2018/19 26
 
3.6%
Other values (11) 149
20.8%

Length

2024-10-28T12:17:31.055250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2024 83
11.6%
2022 72
10.1%
2023 65
9.1%
2023/24 61
8.5%
2021/22 57
 
8.0%
2022/23 57
 
8.0%
2019/20 53
 
7.4%
2019 52
 
7.3%
2021 40
 
5.6%
2018/19 26
 
3.6%
Other values (11) 149
20.8%

Most occurring characters

ValueCountFrequency (%)
2 1644
42.3%
0 791
20.4%
1 424
 
10.9%
/ 342
 
8.8%
3 183
 
4.7%
4 173
 
4.5%
9 131
 
3.4%
5 65
 
1.7%
8 53
 
1.4%
6 43
 
1.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3886
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 1644
42.3%
0 791
20.4%
1 424
 
10.9%
/ 342
 
8.8%
3 183
 
4.7%
4 173
 
4.5%
9 131
 
3.4%
5 65
 
1.7%
8 53
 
1.4%
6 43
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3886
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 1644
42.3%
0 791
20.4%
1 424
 
10.9%
/ 342
 
8.8%
3 183
 
4.7%
4 173
 
4.5%
9 131
 
3.4%
5 65
 
1.7%
8 53
 
1.4%
6 43
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3886
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 1644
42.3%
0 791
20.4%
1 424
 
10.9%
/ 342
 
8.8%
3 183
 
4.7%
4 173
 
4.5%
9 131
 
3.4%
5 65
 
1.7%
8 53
 
1.4%
6 43
 
1.1%

AveRPW
Real number (ℝ)

HIGH CORRELATION 

Distinct604
Distinct (%)84.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.841049
Minimum0
Maximum139
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size5.7 KiB
2024-10-28T12:17:31.603746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile9.877
Q116.365
median20.8
Q325.76
95-th percentile36.033
Maximum139
Range139
Interquartile range (IQR)9.395

Descriptive statistics

Standard deviation9.4860309
Coefficient of variation (CV)0.43432121
Kurtosis34.268737
Mean21.841049
Median Absolute Deviation (MAD)4.63
Skewness3.4459078
Sum15616.35
Variance89.984781
MonotonicityNot monotonic
2024-10-28T12:17:32.004574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16.3 5
 
0.7%
29.2 3
 
0.4%
18.42 3
 
0.4%
22.9 3
 
0.4%
14.11 3
 
0.4%
28.72 3
 
0.4%
18.5 3
 
0.4%
21.87 3
 
0.4%
24.28 3
 
0.4%
13.2 3
 
0.4%
Other values (594) 683
95.5%
ValueCountFrequency (%)
0 1
0.1%
3.35 1
0.1%
3.56 1
0.1%
4.04 1
0.1%
5 1
0.1%
5.31 1
0.1%
5.81 1
0.1%
6.05 1
0.1%
6.08 2
0.3%
6.15 1
0.1%
ValueCountFrequency (%)
139 1
0.1%
75.75 1
0.1%
64.46 1
0.1%
58.33 1
0.1%
54.85 1
0.1%
51.5 1
0.1%
51.23 1
0.1%
50.83 1
0.1%
50.5 1
0.1%
49.1 1
0.1%

AveRPO
Real number (ℝ)

HIGH CORRELATION 

Distinct385
Distinct (%)53.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.3296643
Minimum0
Maximum13.43
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size5.7 KiB
2024-10-28T12:17:32.370418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4.744
Q16.505
median7.51
Q38.275
95-th percentile9.37
Maximum13.43
Range13.43
Interquartile range (IQR)1.77

Descriptive statistics

Standard deviation1.4806197
Coefficient of variation (CV)0.20200375
Kurtosis1.5941745
Mean7.3296643
Median Absolute Deviation (MAD)0.87
Skewness-0.42839127
Sum5240.71
Variance2.1922346
MonotonicityNot monotonic
2024-10-28T12:17:32.713065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.28 7
 
1.0%
8.51 7
 
1.0%
7.01 6
 
0.8%
7.95 6
 
0.8%
7.02 6
 
0.8%
8.27 6
 
0.8%
7.55 6
 
0.8%
7.43 6
 
0.8%
7.72 5
 
0.7%
7.82 5
 
0.7%
Other values (375) 655
91.6%
ValueCountFrequency (%)
0 1
0.1%
2.52 1
0.1%
2.67 1
0.1%
3.04 1
0.1%
3.12 1
0.1%
3.13 1
0.1%
3.2 1
0.1%
3.3 1
0.1%
3.42 1
0.1%
3.48 1
0.1%
ValueCountFrequency (%)
13.43 1
0.1%
12.82 1
0.1%
12.54 1
0.1%
11.75 1
0.1%
10.63 1
0.1%
10.51 1
0.1%
10.49 1
0.1%
10.44 1
0.1%
10.38 2
0.3%
10.32 1
0.1%

Interactions

2024-10-28T12:17:17.710419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:16:55.230799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:16:58.126233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:17:00.452142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:17:02.537519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:17:04.537705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:17:06.545771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:17:08.710629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:17:11.468360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:17:14.919252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:17:17.912409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:16:55.596984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:16:58.316145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:17:00.632925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:17:02.734786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:17:04.704412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:17:06.744236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:17:08.908919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:17:11.722098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:17:15.193260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:17:18.151878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:16:55.918090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:16:58.572777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:17:00.811435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:17:02.946201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:17:04.928142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:17:06.947434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:17:09.117728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:17:12.100234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:17:15.481916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:17:18.400497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:16:56.257307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:16:58.801720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:17:00.985318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:17:03.125556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:17:05.153215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:17:07.198582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:17:09.311442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:17:12.552299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:17:15.729377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:17:18.619954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:16:56.487388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:16:59.072797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:17:01.159915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:17:03.310771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:17:05.355913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:17:07.464314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:17:09.511568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:17:12.837281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:17:15.970333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:17:18.836431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:16:56.765035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:16:59.294659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:17:01.388592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:17:03.496706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:17:05.545609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:17:07.736775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:17:09.716128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:17:13.274052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:17:16.237612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:17:19.060643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:16:57.033502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:16:59.535623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:17:01.681720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:17:03.822201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:17:05.714278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:17:07.954589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:17:10.014935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:17:13.543988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:17:16.507131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:17:19.295310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:16:57.295246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:16:59.737170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:17:01.943829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:17:04.004507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:17:05.901273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:17:08.134511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:17:10.344376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:17:13.924473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:17:16.838796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:17:19.487106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:16:57.634258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:16:59.934209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:17:02.158421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:17:04.181152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:17:06.081928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:17:08.301288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:17:10.694100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:17:14.267712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:17:17.215119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:17:19.832537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:16:57.897134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:17:00.198016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:17:02.348330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:17:04.351776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:17:06.258415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:17:08.507634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:17:11.014705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:17:14.582582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-10-28T12:17:17.470295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-10-28T12:17:32.989073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
AveRPOAveRPWHSInnsLSLostMatNRRunsSeasonTiedW/LWon
AveRPO1.0000.8560.7920.1530.024-0.3020.1550.0400.4050.1400.0000.5530.455
AveRPW0.8561.0000.6830.167-0.143-0.3560.1660.0000.3900.1670.0000.6690.535
HS0.7920.6831.0000.4690.1160.0300.4650.0350.6710.1120.0370.4780.602
Inns0.1530.1670.4691.0000.2420.6480.9970.1860.9400.1790.1850.2360.752
LS0.024-0.1430.1160.2421.0000.2950.2460.0690.2580.0700.156-0.0960.099
Lost-0.302-0.3560.0300.6480.2951.0000.6460.0540.5170.0830.039-0.5260.061
Mat0.1550.1660.4650.9970.2460.6461.0000.2250.9360.1690.1790.2360.750
NR0.0400.0000.0350.1860.0690.0540.2251.0000.1570.1820.0000.0500.128
Runs0.4050.3900.6710.9400.2580.5170.9360.1571.0000.1080.1960.3540.807
Season0.1400.1670.1120.1790.0700.0830.1690.1820.1081.0000.0000.0160.138
Tied0.0000.0000.0370.1850.1560.0390.1790.0000.1960.0001.0000.0450.125
W/L0.5530.6690.4780.236-0.096-0.5260.2360.0500.3540.0160.0451.0000.775
Won0.4550.5350.6020.7520.0990.0610.7500.1280.8070.1380.1250.7751.000

Missing values

2024-10-28T12:17:20.654477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-10-28T12:17:21.274281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

TeamMatWonLostTiedNRW/LRunsInnsHSLSSeasonAveRPWAveRPO
0Zimbabwe202000.0000002582132.0126.02018/1915.176.91
1Zimbabwe1155011.000000142311174.0115.02022/2316.947.13
2Zimbabwe202000.0000002612141.00.02017/1818.646.52
3Zimbabwe15510000.500000225715190.0127.02015/1620.157.59
4Zimbabwe1697001.285714244816236.095.0202222.457.75
5Zimbabwe1789000.888889247617217.082.02023/2424.037.81
6Zimbabwe514000.2500007345175.00.0201523.677.34
7Zimbabwe606000.0000008536156.0148.02020/2119.387.28
8Zimbabwe550005.00000010905344.00.02024/2543.6012.82
9Zimbabwe633001.0000009096177.0152.02019/2024.567.98
TeamMatWonLostTiedNRW/LRunsInnsHSLSSeasonAveRPWAveRPO
705Afghanistan1376001.166667181313189.00.0202224.837.74
706Afghanistan522011.0000006145154.0116.0202317.546.69
707Afghanistan641104.0000008966184.00.02019/2022.407.79
708Afghanistan550005.0000007675167.00.0201823.247.92
709Afghanistan330003.0000006243278.00.02018/1941.6010.51
710Afghanistan101000010.000000161910233.00.02016/1733.728.92
711Afghanistan422001.0000006544197.00.0201925.158.17
712Afghanistan853001.66666710108183.056.0202418.367.04
713Afghanistan651005.0000009596210.00.0201531.968.37
714Afghanistan17125002.400000278417215.0160.02015/1624.858.27